Supervised
Runs checks on its own and asks Lead Dev before it writes code.
Your organization writes the policy. And your workforce works inside it
Policy result: Ask. The work returns with the decision and context attached.
Action handling, model bounds, spend ceilings, what the workforce is allowed to learn, and the record of every change are all set once at the organization and inherited by every project. Autonomy is the baseline, and the consequences stay yours to decide.
AI software engineering at enterprise scale runs on orchestration, security, and governance
At enterprise scale, the work stops being one engineer coaxing a model in a chat window and becomes a managed workforce. Lead Dev orchestrates the specialists, security controls remain active, and governance keeps action outcomes and review paths visible.
The velocity your devs want. The control your CISO needs
Adopt autonomous AI without betting the company on it. Your organization sets action policy, branch policy decides where work can land, and model bounds decide which approved routes the workforce can use. When covered work needs judgment, it returns the call to your team.
Hard limits stay hard
Protected branches, production credentials, and spend limits remain separate from specialist capability.
Access scoped to the work
Connected repositories, approved context, Secrets, and temporary credential exposure follow their own project and organization controls.
Model bounds owned centrally
Your organization sets the model bounds: approved models, provider rules, and spend policy. Profiles choose how work runs inside them.
Governed actions stay on the record
Recorded approvals, checks, stops, and governed actions stay attached to the work they touched.
Autonomy is the baseline
Every specialist can carry assigned work through implementation and verification. Choose how closely Lead Dev reviews its actions. Project and organization policies can require further review or block an action.
Trusted
Writes code and runs checks on its own. Wider actions go to Lead Dev first.
Autonomous
Runs work independently, including commands, packages, and delegation.
Spending is governed like every other action
The organization sets what may be spent, a project can hold something lower, and a single specialist can be narrower still. Work that reaches a ceiling follows the review path an admin chose rather than stopping quietly.
Explore Spend ControlsBudget
Every ceiling nests inside the one above it
Organization
The ceiling every project inherits
Project
Lower for a project that should spend less
Specialist
Lower again for one specialist
Thread
Drawn from what is left above it
Objective
Drawn from what is left above it
How production-grade software gets made at AI scale
Start with a conversation that goes far beyond architecture and into business needs, too
Talk through the customer need, business pressure, product feel, and technical path. Lead Dev turns the conversation into a spec your team can steer before code starts.
Adversarial reviews are part of the everyday workflow
Lead Dev checks assumptions, edge cases, standards, and high-stakes calls. When a decision needs another check, click and request a Second Opinion with quick select for any model anytime.
Quality review and deterministic security
Lead Dev reviews the result against your Quality Standards, while deterministic security controls enforce what agents may change. Your team can inspect the checks and reasoning beside the returned work.
Specialists use the product before your customers do
Specialists use the product by simulating real users in the browser, find broken flows, and learn from what your team accepts, corrects, or sends back.